Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 5 references
Abstract
An explainable hybrid system model is proposed for detecting non-sufficient funds (NSF) check risks. Data leakage cleaning and feature engineering were applied to create a derived feature set. Multiple ensemble machine learning models were compared using stratified k-fold cross-validation, with LightGBM achieving the highest performance (F1-Makro: 0.7333; ROC-EAA: 0.9212). The best model’s predictions were presented to open-source large language models using zero-shot, few-shot, and chain-of-thought prompting strategies to generate natural language risk explanations. Asymmetric score ranges and strategy-specific thresholds were designed to mitigate central tendency bias. A rule-based scoring system was developed as a baseline. With appropriate prompting strategies, large language models provided competitive results compared to both rule-based systems and direct classification; the best performance was achieved by Qwen 32B with chain-of-thought prompting (F1-Makro: 0.8670; Dengeli Doğruluk: 0.8776).
The results show that the ensemble techniques are a practical approach to boost the precision of LLMs in the detection of vulnerabilities and suggest that ensemble methods offer great potential in the advancement of software security analysis.
H. Al-Ofeishat, Azhar Hussain, M. Faheem et al.· Engineering, Technology &...· 0 citations
Prompt escalation can increase the resource requirements of lightweight large language models (LLMs) without improving predictive performance. We evaluated four instruction-tuned 2–4B models on Grade School Math 8K (GSM8K), CommonsenseQA (CSQA), Recognizing Textual Entailment (RTE), and the binary Stanford Sentiment Tr...
Seyoung Kim, Bonggyun Ko· Applied Sciences· 0 citations
AssistEM, a framework for efficient LLM adaptation to EM via principled data selection, demonstrates that selective fine-tuning not only accelerates adaptation but also improves training efficiency (requiring fewer GPU hours), enabling open-source LLMs to rival–and in some cases outperform–closed-source models.
John Bosco Mugeni, Steven J. Lynden, Toshiyuki Amagasa et al.· International Journal of Dat...· 1 citation
The results show that the LLM's capability dissolves with dimension in a way no noise-corrupted classical learner mimics - which explains why LLMs, so capable elsewhere, keep losing to fifty-year-old baselines on tables, while leaving the mechanism of the prediction as an open question.
This study benchmarks machine learning (ML) and deep learning models for intrusion detection using the publicly available FWAF dataset, and emphasizes the value of balancing performance with interpretability, empowering Security Operations Centers (SOC) to validate automated decisions and foster trustworthy AI-driven w...
Fatih Ünlü, Y. Sönmez, Murat Dener· El-Cezeri Fen ve Mühendislik...· 0 citations
This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a zero-shot framework, without labeled training data. The problem is formulated as a binary severity classification task across three forecast horizons (3h, 6h, 12h), using s...
Christos Petridis, Z. Obradovic, M. Kezunović· 0 citations
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